Today we are thinking about what a good presentation or visualization of data is like. It is important that the data we use and consume is useful to those who use it and informative about what we want to know. We ourselves have used a few light metrics to test the initial reactions of visual presentations to clients and two of them are eye-catching and easy to understand. If at first glance you do not understand what data the presentation presents and know how to use it, the report is far too complex, difficult or incompletely structured. But these will be added in more detail by the individual factors involved.
Perhaps the single most important factor in data analysis is simplicity and clarity. This means that not all data or possible figures should be displayed in the same presentation. If millions of rows of data are displayed, it is clear that up to hundreds of different results can be formulated from there, but this is not to be done. It is better to do three reports or visualization for different target groups than one report that serves everyone.
The next important factors will be the terms used and reliability. If there is a risk that users of the presentation will misinterpret the terms, there is no uniform terminology, or different variables can be calculated using several different formulas, it is important that all of these are explained to the user. Everyone needs to know unambiguously what the terms used are and with what formulas the different results have been calculated. Think about what this means, for example, selling or exchanging data if you think you are buying data on something but the supplier has calculated the figures on the data in a way that is harmful to you and you don’t know it?
When visualizing a large amount of data, it is also important to understand to highlight from all possible factors the "exceptions" to the data to which the reader should pay attention. Significant efforts must also be made to ensure the accuracy of these factors. If, for example, the most important factors in the management of a production plant are the exceptions and the reasons behind the consequences, it is important that these are clearly highlighted, which significantly increases the value of visualization. In addition, the user must know the accuracy and model with which the data was produced and generalizations can be made. For example, in SLA (Service Level Agreement) reporting, there are dozens of different formulas and time spans, so when reporting these, you need to know which time period the data applies to and how it is calculated.
The layout in visualization and presentations must be carefully considered. Even if you have the best data in the world and one that you would get money from others but it looks like something your customers don’t want to use, the value of the data is easily wasted. But also the other way around, if visualization leaves the user with a Wau effect in terms of visualization and usability, it’s really easy to keep developing and get users excited.